Unsupervised Learning-Based 3-D Target Reconstruction From Single-View SAR Image

Three-dimensional shape retrieval from synthetic aperture radar (SAR) images has long presented a significant research challenge, with single-view reconstruction proving even more complex due to constraints such as the scarcity of labeled data, limited sample diversity, and heightened sensitivity to radar scattering characteristics. Recently developed deep learning-based methods have made progress in single-view target reconstruction from SAR images. However, these methods still rely heavily on 3-D ground-truth supervision and fail to fully leverage SAR imaging mechanisms for 3-D reconstruction. To address these limitations, an end-to-end unsupervised single-view 3-D reconstruction framework based on a differentiable SAR renderer (DSR) is proposed, achieving precise reconstruction while eliminating the need for ground-truth data. Specifically, the encoder-decoder architecture effectively extracts 3-D and angular features, utilizing template deformation to preserve both fine details and global structures across scales, along with essential pose information for 3-D shape reconstruction. The reconstructed 3-D model is projected onto a 2-D plane, and pixel-level intersection over union (PIoU) loss is employed for unsupervised learning, enabling the extraction of discriminative latent structures and patterns. This approach effectively reduces low-frequency noise, sharpens critical edges, and enhances high-frequency details, improving spatial structure accuracy while minimizing shape distortions and height errors in complex targets. Extensive quantitative and qualitative experiments on both simulated and real datasets demonstrate the framework's superior performance in single-view SAR 3-D target reconstruction, offering a promising solution with broad potential applications.